SOTAVerified

Activity Recognition

Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.

Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters

Papers

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Showing 101–150 of 1322 papers

TitleStatusHype
Self-supervised transfer learning of physiological representations from free-living wearable dataCode1
Learning Generalizable Physiological Representations from Large-scale Wearable DataCode1
HHAR-net: Hierarchical Human Activity Recognition using Neural NetworksCode1
Improved Actor Relation Graph based Group Activity RecognitionCode1
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor DataCode1
SeCo: Exploring Sequence Supervision for Unsupervised Representation LearningCode1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor dataCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWBCode1
Human Activity Recognition from Wearable Sensor Data Using Self-AttentionCode1
Gimme Signals: Discriminative signal encoding for multimodal activity recognitionCode1
Convolutional Tensor-Train LSTM for Spatio-temporal LearningCode1
Privacy and Utility Preserving Sensor-Data TransformationsCode1
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity UnderstandingCode1
Mobile Sensor Data AnonymizationCode1
Semi-Supervised Online Structure Learning for Composite Event RecognitionCode1
Protecting Sensory Data against Sensitive InferencesCode1
Multivariate LSTM-FCNs for Time Series ClassificationCode1
Real-world Anomaly Detection in Surveillance VideosCode1
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data ProcessingCode1
OSL𝛼: Online Structure Learning Using Background Knowledge AxiomatizationCode1
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofsβ€”0
SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition NetworkCode0
Efficient Retail Video Annotation: A Robust Key Frame Generation Approach for Product and Customer Interaction Analysisβ€”0
DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understandingβ€”0
MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognitionβ€”0
AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environmentsβ€”0
ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUsβ€”0
Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniquesβ€”0
Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural NetworksCode0
Spatiotemporal Analysis of Forest Machine Operations Using 3D Video Classificationβ€”0
Knowledge Distillation for Reservoir-based Classifier: Human Activity Recognitionβ€”0
Predicting Human Depression with Hybrid Data Acquisition utilizing Physical Activity Sensing and Social Media Feedsβ€”0
A Probabilistic Jump-Diffusion Framework for Open-World Egocentric Activity Recognitionβ€”0
MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Trackingβ€”0
Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic DatasetCode0
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity RecognitionCode0
Recognition of Physiological Patterns during Activities of Daily Living Using Wearable Biosignal Sensorsβ€”0
Label Leakage in Federated Inertial-based Human Activity RecognitionCode0
MoPFormer: Motion-Primitive Transformer for Wearable-Sensor Activity Recognitionβ€”0
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edgeβ€”0
SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognitionβ€”0
PosePilot: An Edge-AI Solution for Posture Correction in Physical Exercisesβ€”0
BiomechGPT: Towards a Biomechanically Fluent Multimodal Foundation Model for Clinically Relevant Motion Tasksβ€”0
Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detectionβ€”0
SPAR: Self-supervised Placement-Aware Representation Learning for Multi-Node IoT Systemsβ€”0
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUsβ€”0
FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environmentsβ€”0
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All datasetsRWF-2000Stanford40First-Person Hand Action BenchmarkSelf-stimulatory Behavior Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy93.4β€”Unverified
2Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics datasetAccuracy90.4β€”Unverified
3Human Skeletons + Change DetectionAccuracy90.25β€”Unverified
4Separable Convolutional LSTMAccuracy89.75β€”Unverified
5SPIL ConvolutionAccuracy89.3β€”Unverified
6Flow Gated NetworkAccuracy87.25β€”Unverified
#ModelMetricClaimedVerifiedStatus
1FocusCLIPTop-3 Accuracy (%)10.47β€”Unverified
2CLIPTop-3 Accuracy (%)6.49β€”Unverified
#ModelMetricClaimedVerifiedStatus
1Boutaleb et al.1:1 Accuracy97.91β€”Unverified
#ModelMetricClaimedVerifiedStatus
1all-landmark-modelActivity Recognition0.76β€”Unverified